Summary of Cogntke: a Cognitive Temporal Knowledge Extrapolation Framework, by Wei Chen and Yuting Wu and Shuhan Wu and Zhiyu Zhang and Mengqi Liao and Youfang Lin and Huaiyu Wan
CognTKE: A Cognitive Temporal Knowledge Extrapolation Framework
by Wei Chen, Yuting Wu, Shuhan Wu, Zhiyu Zhang, Mengqi Liao, Youfang Lin, Huaiyu Wan
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel framework, CognTKE, for reasoning future unknowable facts on temporal knowledge graphs (TKGs). Unlike previous explainable reasoning methods that focus on local temporal paths, CognTKE introduces a novel temporal cognitive relation directed graph (TCR-Digraph) and performs interpretable global shallow reasoning and local deep reasoning over the TCR-Digraph. The framework consists of two components: the global shallow reasoner, which performs one-hop temporal relation reasoning, and the local deep reasoner, which performs complex multi-hop path reasoning. Experimental results on four benchmark datasets show that CognTKE outperforms state-of-the-art baselines in terms of accuracy and achieves excellent zero-shot reasoning ability. The code for CognTKE is available at https://github.com/WeiChen3690/CognTKE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to understand future events based on the relationships between things over time. It creates a special kind of graph that helps us figure out how these events might be connected. The researchers tested their approach using four different datasets and found it worked better than other methods at predicting what might happen next. This is important because it can help us make better decisions by giving us more information about the future. |
Keywords
» Artificial intelligence » Zero shot
